If You Let Them Build It, They Will Stay! An Empirical Study of Add-on Content and User Engagement

This study aims to uncover the effects of two \ types of add-on content –namely, user generated and developer \ generated content– on user engagement with software. Utilizing \ a novel dataset from a major online game distribution platform \ covering 7323 products between January 2015 and February \ 2016, the findings reveal that both types of add-on content \ increases the engagement with software. However, we observe \ substitutive patterns between different types of add-on content. \ Our results suggest integrating social features to the base \ product reduces these substitution effects. The results of this \ study contribute to the literatures on user engagement and \ add-on content by uncovering hitherto overlooked substitutive \ relations between user generated and developer generated add- \ on content

The Role of Religion in Online Prosocial Lending

The Internet has long been argued to have “flattened” the world. A variety of work, however, has shown that cross-border frictions continue to manifest through various individual level differences, e.g., cultural, demographic, and geographic. We extend this literature here, offering a novel consideration of religious differences as a significant barrier to online peer-to-peer transactions in the context of prosocial lending. Specifically, we propose a measure of religious distance between any given pair of countries. We then incorporate this measure into a standard gravity model of trade, which we use to explain country-to-country lending volumes between 2006 and 2017 at kiva.org. We demonstrate the negative and significant effects of religious differences on lending activity over and above other established factors. Moreover, we demonstrate that the effects of religious differences vary a great deal, being moderated by the social environment characterizing both a lender country and borrower country in a given time period. That is, we show that increases in the degree of social hostilities within a lender country amplifies the baseline (negative) effects of religious differences on lending activity. At the same time, we demonstrate that diversity of religion and greater physical distances attenuate the role of religious differences

Text mining MEDLINE to support public health

Today’s society is data rich and information driven, with access to numerous data sources available that have the potential to provide new insights into areas such as disease prevention, personalised medicine and data driven policy decisions. This paper describes and demonstrates the use of text mining tools developed to support public health institutions to complement their data with other accessible open data sources, optimize analysis and gain insight when examining policy. In particular we focus on the exploration of MEDLINE, the biggest structured open dataset of biomedical knowledge. In MEDLINE we utilize its terminology for indexing and cataloguing biomedical information – MeSH – to maximize the efficacy of the dataset

People Don\u27t Change, Their Priorities Do: Evidence of Value Homophily for Disaster Relief

Earlier studies on crowdfunding markets show that a crisis increases the charitable funding for people affected by the crisis. However, these studies fail to explain whether such an increase is purely because of the awareness of need, or, otherwise, there are some behavioral mechanisms associated with disaster relief fundraising. To address this gap, we theoretically and empirically explore the role of value homophily in shifting lending priorities in online prosocial platforms. Considering the full spectrum of cultural influences, we develop the concept of culturalist choice homophily, where value-based similarities emerge based on the culturally-motivated behaviors and historicist choice homophily, where value-based similarities emerge based on similarities in historical-cultural barriers. We hinge on the Arab Spring crisis in a Difference-in-Difference (DID) setting to test our hypotheses. We show that the Arab Spring crisis increased charitable funding from lenders with high emancipative values and similar colonial histories

Machine-assisted Regulation, Online Participation and Human Moderation

Content moderation is on the rise under the current diverse online environment. Machines have been widely used to cope with the increasing need for platform regulation. On the one hand, it enables platforms to scale the regulations regularly. On the other hand, it may result in over-policing and de-humanizing in platform regulations. The role of voluntary human moderators may also change. However, few prior studies have focused on this emerging topic. To fill this research gap, we study the impact of machine-based regulations on users’ and human moderators’ participation. With data collected from 161 subreddits on Reddit, we found that machine-based moderation stimulates more users’ participation. Moreover, delegating moderations to machine augments voluntary community managers’ role as moderators but weaken their role as community users. Human moderators accomplish more moderations. Particularly, they complete more policing; they also offer more explanations and suggestions to their community members

Text mining open datasets to support public health

Today’s society is data rich and information driven, with access to numerous data sources available that have the potential to provide new insights into areas such as disease prevention, personalised medicine and data driven policy decisions. This paper describes and demonstrates the use of text mining tools developed to support public health institutions to complement their data with other accessible open data sources, optimize analysis and gain insight when examining policy. In particular we focus on the exploration of MEDLINE, the biggest structured open dataset of biomedical knowledge. In MEDLINE we utilize its terminology for indexing and cataloguing biomedical information – MeSH – to maximize the efficacy of the dataset

Mining MEDLINE for the visualisation of a global perspective on biomedical knowledge

There is an ever increasing number of data sources that potentially could be used to gain new insights into areas such as disease prevention, policy formulation/evaluation and personalised medicine, but these are not optimised for use within an analytics type user interface. The MIDAS project was funded under a call for ‘Big Data supporting Public Health policies’ to develop a big data platform that facilitates the utilisation of healthcare data beyond existing isolated systems, making that data amenable to enrichment with open and social data. This aligns closely with a number of themes in Knowledge Discovery in Databases (KDD) in that the platform enables the integration of heterogeneous data sources, providing privacy-preserving analytics, forecasting tools and visualisation modules to deliver actionable information. Policy makers as a result will have the capability to perform data-driven evaluations of the efficiency and effectiveness of proposed policies in terms of expenditure, delivery, wellbeing, and health and socio-economic inequalities, thus improving current policy formulation, delivery risk stratification and evaluation. This H2020 project has a total of 15 partners from 5 EU countries as well as Arizona State University (ASU). The partners are Universities, SMEs and health departments in governmental institutions